datahub/docs/features/feature-guides/mcp.md

# DataHub MCP Server

The DataHub MCP Server implements the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction), which standardizes how applications provide context to LLMs and AI agents. This enables AI agents to query DataHub metadata and use it to find relevant assets, traverse lineage, and more.

Want to learn more about the motivation, architecture, and advanced use cases? Check out our [deep dive blog post](https://datahub.com/blog/datahub-mcp-server-block-ai-agents-use-case/).

There's two ways to use the MCP server, which vary in setup required but offer the same capabilities.

- [Managed MCP Server](#managed-mcp-server-usage) - Available on DataHub Cloud v0.3.12+
- [Self-Hosted MCP Server](#self-hosted-mcp-server-usage) - Available for DataHub Core

## Capabilities

**Search for Data** <br />
Find the right data to use for your projects & analysis by asking questions in plain English - skip the tribal knowledge.

**Dive Deeper** <br />
Separate the signal from noise with rich context about your data, including usage, ownership, documentation, tags, and quality.

**Lineage & Impact Analysis** <br />
Understand the impact of upcoming changes to tables, reports, and dashboards using DataHub’s end-to-end lineage graph.

**Query Analysis & Authoring** <br />
Understand how your mission-critical data is typically queried, or build custom queries for your tables.

**Works Where You Work** <br />
Seamlessly integrates with AI-native tools like Cursor, Windsurf, Claude Desktop, and OpenAI to supercharge your workflows.

## Managed MCP Server Usage

For folks on DataHub Cloud v0.3.12+, you can use our hosted MCP server endpoint.

:::note

The managed MCP server endpoint is only available with DataHub Cloud v0.3.12+. For DataHub Core and older versions of DataHub Cloud, you'll need to [self-host the MCP server](#self-hosted-mcp-server-usage).

:::

### Prerequisites

To connect to the MCP server, you'll need the following:

- The URL of your DataHub Cloud instance e.g. `https://<tenant>.acryl.io`
- A [personal access token](../../authentication/personal-access-tokens.md)

Your hosted MCP server URL is `https://<tenant>.acryl.io/integrations/ai/mcp/?token=<token>`.

### Configure

<details>
  <summary>Claude Desktop</summary>

1. Open your `claude_desktop_config.json` file. You can find it by navigating to Claude Desktop -> Settings -> Developer -> Edit Config.
1. Update the file to include the following content. Be sure to replace `<tenant>` and `<token>` with your own values.

```json
{
  "mcpServers": {
    "datahub-cloud": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://<tenant>.acryl.io/integrations/ai/mcp/?token=<token>"
      ]
    }
  }
}
```

</details>

<details>
  <summary>Cursor</summary>

1. Navigate to Cursor -> Settings -> Cursor Settings -> MCP -> add a new MCP server
2. Enter the following into the file:

```json
{
  "mcpServers": {
    "datahub-cloud": {
      "url": "https://<tenant>.acryl.io/integrations/ai/mcp/?token=<token>"
    }
  }
}
```

3. Once you've saved the file, confirm that the MCP settings page shows a green dot and a couple tools associated with the server.

</details>

<details>
  <summary>Other</summary>

Most AI tools support remote MCP servers. For those, you'll typically need to:

- Provide the hosted MCP server URL: `https://<tenant>.acryl.io/integrations/ai/mcp/?token=<token>`
- Ensure that the authentication mode is _not_ set to "OAuth" (if applicable)

For AI tools that don't yet support remote MCP servers, you can use the `mcp-remote` tool to connect to the MCP server.

- Command: `npx`
- Args: `-y mcp-remote https://<tenant>.acryl.io/integrations/ai/mcp/?token=<token>`

</details>

## Self-Hosted MCP Server Usage

You can run the [open-source MCP server](https://github.com/acryldata/mcp-server-datahub) locally.

### Prerequisites

1. Install [`uv`](https://github.com/astral-sh/uv)

   ```bash
   # On macOS and Linux.
   curl -LsSf https://astral.sh/uv/install.sh | sh
   ```

2. For authentication, you'll need the following:

   - The URL of your DataHub instance's GMS endpoint; e.g. `http://<localhost>:8080` or `https://<tenant>.acryl.io`
   - A [personal access token](../../authentication/personal-access-tokens.md)

### Configure

<details>
  <summary>Claude Desktop</summary>

1. Run `which uvx` to find the full path to the `uvx` command.

1. Open your `claude_desktop_config.json` file. You can find it by navigating to Claude Desktop -> Settings -> Developer -> Edit Config.

1. Update the file to include the following content. Be sure to replace `<tenant>` and `<token>` with your own values.

```js
{
  "mcpServers": {
    "datahub": {
      "command": "<full-path-to-uvx>",  // e.g. /Users/hsheth/.local/bin/uvx
      "args": ["mcp-server-datahub"],
      "env": {
        "DATAHUB_GMS_URL": "<your-datahub-url>",
        "DATAHUB_GMS_TOKEN": "<your-datahub-token>"
      }
    }
  }
}
```

</details>

<details>
  <summary>Cursor</summary>

1. Navigate to Cursor -> Settings -> Cursor Settings -> MCP -> add a new MCP server
1. Enter the following into the file:

```json
{
  "mcpServers": {
    "datahub": {
      "command": "uvx",
      "args": ["mcp-server-datahub"],
      "env": {
        "DATAHUB_GMS_URL": "<your-datahub-url>",
        "DATAHUB_GMS_TOKEN": "<your-datahub-token>"
      }
    }
  }
}
```

3. Once you've saved the file, confirm that the MCP settings page shows a green dot and a couple tools associated with the server.

</details>

<details>
  <summary>Other</summary>

For other AI tools, you'll typically need to provide the following configuration:

- Command: `uvx`
- Args: `mcp-server-datahub`
- Env:
  - `DATAHUB_GMS_URL`: `<your-datahub-url>`
  - `DATAHUB_GMS_TOKEN`: `<your-datahub-token>`

</details>